{
  "counts": {
    "adjacent": 19,
    "core": 3,
    "errors": 0,
    "negative": 65,
    "total": 87
  },
  "date": "2026-07-30",
  "errors": [],
  "fresh_content_days": 21,
  "generated_at": "2026-07-30T17:03:41Z",
  "items": [
    {
      "age_days": 6,
      "arxiv_id": "2607.22511",
      "authors": [
        "Jiyuan Tan",
        "Vasilis Syrgkanis"
      ],
      "content_date": "2026-07-24",
      "freshness": "fresh",
      "id": "arxiv:2607.22511",
      "kind": "paper",
      "label": "core",
      "matched_signals": [
        "mathlib_retrieval",
        "tool_use_agents",
        "verifier_guided_reasoning"
      ],
      "published": "2026-07-24",
      "score": 7.9,
      "source": "arxiv-ai4math-core",
      "summary": "Automating theoretical research is constrained not only by the generation of candidate results, but also by their reliable evaluation. A common approach is to close the research loop with a large language model (LLM) reviewer. However, such reviewers remain empirically unreliable: they may accept fabricated papers and detect them at rates close to chance (Bad Scientist, 2025). We present CausalForge, a framework for automated theoretical research in causal inference grounded in the Lean proof assistant. CausalForge combines Causalean, a foundational Lean library for causal inference containing 7,035 machine-checked declarations developed with language-model assistance under human design and review, with CausalSmith, a self-improving agentic pipeline that selects research topics, proposes results, formalizes statements, constructs proofs, and presents the resulting artifacts for human inspection. Because a machine-checked proof establishes only that a formal statement follows from its assumptions, not that the statement faithfully captures the intended scientific claim, the pipeline augments kernel verification with a statement audit that compares each formal theorem against the informal claim it is intended to express. We evaluate the system using artifacts produced by completed autonomous research runs. The source code, formal library, and run records are available at https://github.com/Jiyuan-Tan/CausalForge.",
      "title": "CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference",
      "updated": "2026-07-24",
      "url": "https://arxiv.org/abs/2607.22511"
    },
    {
      "age_days": 5,
      "arxiv_id": "2607.23390",
      "authors": [
        "Mojtaba Soltanalian"
      ],
      "content_date": "2026-07-25",
      "freshness": "fresh",
      "id": "arxiv:2607.23390",
      "kind": "paper",
      "label": "core",
      "matched_signals": [
        "lean_formal_proving_agents",
        "verifier_guided_reasoning"
      ],
      "published": "2026-07-25",
      "score": 6.5,
      "source": "arxiv-ai4math-core",
      "summary": "When can additional low-bit residual computation replace missing numerical precision for a fixed input-output map? We model a quantized residual system over a fixed horizon as a pure schedule selecting fields from a declared low-bit operation library, and use relaxed controls to characterize its infinite-depth limit. The distance from the target to the closed relaxed reachable set is the exact structural floor: no increase in depth can remove it for that library. Pure schedules approach the relaxed class at rate $O(D^{-1})$ under bounded-variation time dependence and $O(D^{-\\vartheta}+D^{-1})$ under Holder dependence of exponent $\\vartheta$. Execution arithmetic can reverse this conclusion: full-state write-back introduces a $Dρ_z$ penalty and can freeze residual updates, whereas increment error feedback replaces this growth by a bounded carry term and obeys an exact common-lattice conservation law. A fixed-teacher converse makes this rate sharp: for coherent depth-$L$ first-order high-precision comparators, accuracy matching requires $D=Θ(L)$. Learned codebooks add a metadata resource, while state-dependent routing introduces hybrid event conditions. Verified primal and dual bounds yield feasible, impossible, or unresolved decisions before training. Companion software implements the workflow, and Lean 4 machine-checks the exact discrete core. Depth replaces precision only relative to a declared library, horizon, execution semantics, and routing model.",
      "title": "When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation",
      "updated": "2026-07-25",
      "url": "https://arxiv.org/abs/2607.23390"
    },
    {
      "age_days": 7,
      "arxiv_id": "2607.21533",
      "authors": [
        "Lei Zhang",
        "Yusheng Zhao",
        "Yimeng Cao",
        "Ranyiliu Chen",
        "Mingrui Jing",
        "Jizhe Lai",
        "Ziao Tang",
        "Jingu Xie",
        "Hongshun Yao",
        "Xuanqiang Zhao",
        "Guocheng Zhen",
        "Chengkai Zhu",
        "Xin Wang"
      ],
      "content_date": "2026-07-23",
      "freshness": "fresh",
      "id": "arxiv:2607.21533",
      "kind": "paper",
      "label": "core",
      "matched_signals": [
        "lean_formal_proving_agents",
        "verifier_guided_reasoning"
      ],
      "published": "2026-07-23",
      "score": 6.5,
      "source": "arxiv-ai4math-core",
      "summary": "Formal verification is becoming increasingly practical for quantum computing, yet the ability of AI agents to construct machine-checkable proofs in this domain remains unmeasured. We introduce Lean-QuantumAlg-Bench and Lean-QIT-Bench, two Lean 4 benchmarks containing 36 and 40 theorem-completion tasks for quantum algorithms and quantum information theory, respectively. Every task compiles in a fixed environment and is evaluated by deterministic proof checking and targeted semantic review, with difficulty weights assigned before model execution. We evaluate four models-GPT-5.5, Kimi K3, DeepSeek V4-Pro, and MiniMax M3-within a common theorem-proving framework under two settings: a task-only baseline and library-augmented deduction (LAD), which additionally provides access to a verified domain library. The highest difficulty-weighted scores are 60.4 out of 100 on the quantum-algorithm benchmark and 59.6 out of 100 on the quantum-information benchmark. LAD improves both score and completion rate in all eight model-benchmark comparisons, with gains of up to 15.9 points, providing evidence that verified libraries can strengthen domain-specific proof agents. The results reveal recurring weaknesses of agentic proving in areas such as quantum simulation, quantum learning, quantum information measures, and entanglement theory. Monetary and wall-clock costs per score point also vary substantially across models, highlighting important capability-efficiency trade-offs. We expect these benchmarks to establish a reproducible baseline for developing more capable and reliable proof agents, and to pave the way toward self-evolving AI scientists for advancing quantum information science.",
      "title": "Benchmarking Agents for Proving Theorems in Quantum Algorithms and Quantum Information",
      "updated": "2026-07-23",
      "url": "https://arxiv.org/abs/2607.21533"
    },
    {
      "age_days": 4,
      "arxiv_id": "2607.23664",
      "authors": [
        "Mikko Fischer"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23664",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "general_ai_math_reasoning",
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-26",
      "score": 4.9,
      "source": "arxiv-ai4math-core",
      "summary": "We exhibit a connected graph on 24 vertices with maximum degree 3, independence number 9 and zero forcing number 11, refuting a 2017 conjecture of TxGraffiti recorded as Conjecture 2 of the survey of Davila, Brimkov and Pepper. The same construction with a different gadget gives a connected cubic graph on 36 vertices with independence number 15 and zero forcing number 17; the conjecture therefore fails also in the cubic form in which the survey's Lean 4 appendix states it. In particular Z <= alpha + 1 is not a universal bound for connected cubic graphs, and the value Z = alpha + 2 is attained.",
      "title": "A counterexample to the zero forcing versus independence conjecture for cubic and subcubic graphs",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23664"
    },
    {
      "age_days": 1,
      "arxiv_id": "2607.26413",
      "authors": [
        "Shayaan Siddique",
        "Ibrahim Mian"
      ],
      "content_date": "2026-07-29",
      "freshness": "fresh",
      "id": "arxiv:2607.26413",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-29",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "The best known lower bound for the minimum Kochen-Specker vector system in $\\mathbb{R}^3$ -- 24 vectors -- rests on a computational proof whose combinatorial half emits DRAT proofs but whose geometric half does not: the non-embeddability of thousands of candidate graphs is established by Z3's nonlinear real arithmetic, which produces no checkable proof objects. We close this gap for the proof's blocking database. We introduce exact rational case-tree certificates of real non-embeddability, whose splits are polynomial factorizations and rational sum-of-squares decompositions and whose leaves are discharged by injectivity, ideal-membership, or Positivstellensatz-shaped positivity arguments, and we certify all 291 source lines (180 distinct graphs) of the published pipeline's order-10 to order-13 blocking lists. Certificates are replayed by two independent checkers that share no code with the generator: a pure-Python replay over exact fractions, and a total checker implemented and proved sound in Lean 4. The soundness theorem -- acceptance implies that no injective-on-rays, orthogonality-respecting assignment of nonzero real vectors realizes the graph -- is kernel-checked with axiom closure {propext, Classical.choice, Quot.sound}, and a gcd-free rational arithmetic layer makes the entire verdict computation kernel-reducible, so each per-graph non-embeddability result is a closed kernel theorem proved by decide. The formalization surfaced findings about the published pipeline, including a load-bearing injectivity side condition in its embeddability notion, hidden WLOG case obligations invisible to Z3-based workflows, and an unreproducible candidate count that we resolve against the published artifacts. All certificates, checkers, and proofs are available and replayable from a single build.",
      "title": "Machine-Checked Certificates for the Geometric Half of the Minimum Kochen-Specker Bound",
      "updated": "2026-07-29",
      "url": "https://arxiv.org/abs/2607.26413"
    },
    {
      "age_days": 1,
      "arxiv_id": "2607.26997",
      "authors": [
        "Kuldeep S. Meel",
        "N. V. Vinodchandran",
        "Sourav Chakraborty"
      ],
      "content_date": "2026-07-29",
      "freshness": "fresh",
      "id": "arxiv:2607.26997",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-29",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "We study estimating the size of the union of sets $S_1,\\dots,S_M$, where each $S_i\\subseteqΩ$ is presented implicitly and arrives in a stream. We introduce Delphic sets, a class of streaming problems in which membership, sampling, and counting queries to each set are efficient, and show that this notion captures three well-known problems: Klee's measure problem (discrete version), test coverage estimation in combinatorial testing, and model counting of DNF formulas. Our primary contribution is a simple and efficient sampling-based algorithm that outputs an $(\\varepsilon,δ)$-approximation of the cardinality of the union of Delphic sets in the streaming setting. It has space complexity $O(R\\log|Ω|)$ and update time $O(R\\log R\\cdot\\log(M/δ)\\cdot\\log|Ω|)$, where $R=O(\\log(M/δ)\\cdot\\varepsilon^{-2})$. For the streaming Klee's measure problem, this gives the first algorithm whose update time depends linearly on the dimension $d$ for $d>1$, settling an open problem of Tirthapura and Woodruff (PODS 2012), and it directly yields efficient streaming algorithms for coverage estimation and DNF model counting. We further show that the space for coverage estimation can be made near-optimal at the cost of an update procedure in $\\mathrm{P}^{\\mathrm{NP}}$, revealing a time-space trade-off. A key strength of our approach is the simplicity of both the algorithm and its analysis, which makes it amenable to practical implementation. In this revised version, the algorithm and its correctness analysis have additionally been formalized and machine-checked in Lean 4. (Shortened for Arxiv)",
      "title": "Estimating Size of the Union of Sets in Streaming Model",
      "updated": "2026-07-29",
      "url": "https://arxiv.org/abs/2607.26997"
    },
    {
      "age_days": 2,
      "arxiv_id": "2607.25628",
      "authors": [
        "Ibrahim Mian",
        "Shayaan Siddique"
      ],
      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.25628",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-28",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "The Erdős-Selfridge odd covering problem (Erdős problem #7) asks whether a covering system of $\\mathbb{Z}$ exists whose moduli are all odd, distinct, and greater than 1. The problem is open. We present a Lean 4 formalization, checked end to end by the proof kernel, of the exclusion: any covering of $\\mathbb{Z}$ by finitely many congruence classes with distinct odd moduli > 1 has lcm of the moduli exceeding 10000. The proof composes a formalized density argument (a covering by divisors of $N$ exceeding 1 forces $2N \\le σ_1(N)$, so the lcm is abundant or perfect), a kernel-checked abundancy floor (no odd $N < 945$ qualifies), a family of Chinese-Remainder capacity certificates -- decidable per-$N$ arithmetic inequalities each refuting every covering with distinct moduli > 1 dividing that $N$ -- for all 23 odd abundant numbers below $10^4$, and a kernel-checked enumeration establishing that those 23 are the only odd non-deficient candidates. The result is transported to the official StrictCoveringSystem $\\mathbb{Z}$ formulation of Erdős #7 in google-deepmind/formal-conjectures, with a bidirectional periodicity bridge between coverings of $\\mathbb{Z}$ and finite checks over $\\mathbb{Z}/N\\mathbb{Z}$ suitable for consuming future SAT-style search output. All 63 published theorems depend on exactly propext, Classical.choice, and Quot.sound: no sorry, no native_decide, no solver in the trusted base. The mathematical content is known -- the density argument is folklore, and far larger uncertified classifications of covering numbers exist -- so the contribution is epistemic rather than mathematical: these exclusions are theorems of the Lean kernel, with an axiom gate enforced mechanically in continuous integration.",
      "title": "Kernel-Checked Exclusions for the Erdős-Selfridge Odd Covering Problem: Any Odd Covering of $\\mathbb{Z}$ Has lcm Exceeding 10000",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.25628"
    },
    {
      "age_days": 2,
      "arxiv_id": "2607.26306",
      "authors": [
        "Lefteris Lazaropoulos",
        "Zoe Paraskevopoulou"
      ],
      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.26306",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "verifier_guided_reasoning"
      ],
      "published": "2026-07-28",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Relating low-level executable code to a high-level account of its behavior has been a central concern of programming-language research for decades. From formally verified compilers to translation validators, certifying compilers, and proof-carrying code, each approach chooses between laborious but foundational mechanized proofs and automation that costs completeness, generality, and an increased trusted base. Recently, large language models (LLMs) have begun to change the economics of formal verification. Agentic proof development is now capable of producing machine-checked proofs at a scale and speed that were previously out of reach. In this paper, we evaluate the capabilities of LLMs to produce foundational, machine-checked proofs of refinement between executable code and its high-level specification, as post hoc, per-artifact certificates. We study this in the context of the Ethereum Virtual Machine (EVM), a low-level virtual machine that executes smart contracts on the Ethereum blockchain. We build EquiVM, a foundational framework in Lean comprising an executable EVM semantics and a specification language that characterizes the intended behavior of smart contracts, but commits to no source language or compilation toolchain. In EquiVM, refinement is stated for deployed bytecode of arbitrary provenance, interaction with unknown code is part of the semantics, and each proof is a replayable, machine-checked certificate. No previous technique achieves this combination. Using frontier commercial LLMs, twenty-three real-world contracts are proved end to end with minimal human guidance, among them most of the MakerDAO stablecoin system, at up to a hundred million tokens and a hundred hours of proof time per contract. We conclude that foundational mechanized proofs can now be bought at the price of tokens, and that this shift can reshape how verification frameworks are architected.",
      "title": "Foundational Refinement Proofs for Deployed Bytecode, at the Price of Tokens",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.26306"
    },
    {
      "age_days": 2,
      "arxiv_id": "2607.26230",
      "authors": [
        "Alain Chavarri Villarello",
        "Sander R. Dahmen"
      ],
      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.26230",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-28",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Number fields, which generalize the rational numbers, are fundamental objects in number theory. Many of their key arithmetic properties are captured by invariants whose computation is among the central tasks of computational algebraic number theory and a focus of several computer algebra systems and databases. In this paper, we describe a Lean 4 formalization for certifying several of these number field invariants. Building on previous work on certifying rings of integers, we extend this certification approach to further invariants including the signature, the unit group modulo $p$-th powers, and, ultimately, the class group. We also improve discriminant certification, allowing verifications for higher-degree number fields infeasible in previous work. We introduce structures based on representations of algebraic objects suited to computation, including reusable ones for certifying ideal arithmetic. Along the way, we formalize several underlying mathematical results, for instance on real closed fields and pseudo-remainder sequences, which are of independent interest. We apply our framework to verify hundreds of entries of the $\\textit{L-functions and modular forms database}$ (LMFDB) concerning the discriminant, signature, class number, and class group structure of various number fields. To this end, we wrote a SageMath script that computes the certificates and outputs Lean proofs of the corresponding statements.",
      "title": "Formally certifying number field invariants",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.26230"
    },
    {
      "age_days": 2,
      "arxiv_id": "2607.26344",
      "authors": [
        "Xin Xu",
        "Siru Tao",
        "Kaizhen Tan"
      ],
      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.26344",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-28",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "A gradient-based GNN explainer given a molecule with two chemically equivalent nitro groups assigns them attribution scores that are equal to the last bit. It cannot do otherwise: message passing is exactly permutation equivariant, so any automorphism of the input leaves every attribution invariant. Yet the standard report, the top-k edges, names one of the two, and which one is settled by the order of an array. We show this is a structural obstruction rather than an implementation slip. When no minimal valid explanation is fixed by the input's automorphism group, no rule can be single-valued, minimal and symmetry-respecting at once. For the exact-k reports used in practice we give a parameter-free criterion, mechanised in Lean 4 with no axiom dependencies, that decides from the graph alone whether every score-optimal report of that size must split an orbit. Across 21298 instance-budget decisions the criterion agrees with a mechanical model-equivalence check without exception, and no severing case we found admitted a neutral alternative. The obstruction is common. Nontrivial automorphisms occur in 93.4% of Mutagenicity, the dataset the seminal explainability papers use, so the measure-zero dismissal of symmetric inputs, sound on the continuous domains it was made for, collapses here. At the sparsity budget those papers report, 24.0% of molecules with two interchangeable nitro groups (6 of 25) surface exactly one of them, every one arbitrary under mechanical verification. A model's blindness also manufactures symmetry: every MUTAG molecule contains atoms chemistry separates and the network provably cannot, and a matched control shows the resolution is set by what the model reads rather than how it is parameterised. Reporting orbits removes the arbitrariness at 0.11 ms and 0.43 extra edges per graph.",
      "title": "Automorphism-Induced Non-Canonicity in Top-k Explanations of Graph Neural Networks",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.26344"
    },
    {
      "age_days": 3,
      "arxiv_id": "2607.24483",
      "authors": [
        "Alexander Temerev",
        "Alessio Doria"
      ],
      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "arxiv:2607.24483",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-27",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "We solve Bellman's lost-in-a-forest problem for the golden gnomon $G$, the isosceles triangle with equal sides $1$ and apex angle $108^\\circ$: the shortest curve guaranteed to reach the boundary of $G$ from an unknown starting position and heading is a symmetric seven-piece path of segments, circular shoulders, and tangents, of exactly determined length $C=1.282676025459\\ldots$. To our knowledge, this is the first proved exact optimum for an isosceles triangle whose base angle is below $45^\\circ$. The curve's parameters come from one isolated quartic root, and $C$ is transcendental. Equivalently, $C^{-1}G$ is the smallest homothetic golden-gnomon cover of all unit arcs. The proof introduces a balanced support calibration: one weighted family of escape inequalities, built on the linear relation among the triangle's three normals, exactly saturated by the candidate, through eighteen exact support windows, and confronting every shorter competitor at once. Aggregation along the normal fan compresses the calibration to a finite zero-sum family of supported vectors; summation by parts then bounds its total by path length whenever the running suffix balance, the ledger, stays in the unit disk. A local two-gap surgery and cyclic bitonicity force a shortest hypothetical counterexample into exactly the temporal order the ledger tolerates. Lean 4 verifies the two finite algebraic certificate families and the reusable discrete ledger identities and bounds.",
      "title": "The exact solution of Bellman's lost-in-a-forest problem for the golden gnomon",
      "updated": "2026-07-27",
      "url": "https://arxiv.org/abs/2607.24483"
    },
    {
      "age_days": 3,
      "arxiv_id": "2607.24095",
      "authors": [
        "Ivan Klianev"
      ],
      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "arxiv:2607.24095",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-27",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "In this paper, we resolve the apparent contradiction between the proven possibility of deterministic crash-tolerant consensus in a fully asynchronous environment and the reconfirmation of the FLP impossibility result by Attiya, Castañeda, and Rajsbaum. With the use of a strictly formal framework that extends their reasoning, we close this fundamental gap in theory. Specifically, we demonstrate that a single protocol phase separates their findings from reaching the exact opposite conclusion. Another important outcome is a novel algorithm with ability to tolerate multiple crash faults. We provide a rigorous, strictly formal proof of correctness to validate our results.",
      "title": "Consensus In Asynchrony: Strictly Formal",
      "updated": "2026-07-27",
      "url": "https://arxiv.org/abs/2607.24095"
    },
    {
      "age_days": 3,
      "arxiv_id": "2607.24504",
      "authors": [
        "Cao Nguyen Pham",
        "Oliver Bračevac",
        "Yichen Xu",
        "Yaoyu Zhao",
        "Martin Odersky"
      ],
      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "arxiv:2607.24504",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-27",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Capture checking in Scala 3 enables lightweight and practical effect and resource tracking by recording capabilities in types. However, the system offers no way to reason about kinds of capabilities. Natural constraints such as \"retaining only the control-flow capabilities of this closure\" or \"excluding all thread-local capabilities from this argument\" become inexpressible. Both arise in the Scala 3 standard library: \"Try\" re-throws caught exceptions, so it retains only the control-flow capabilities of its body, and \"Future\" must not capture thread-local resources. The inability to state these constraints has kept parts of the library outside capture checking. We introduce capability classifiers: a tree-structured, user-extensible hierarchy of tags that classify capabilities by their semantic role. Projections filter capture sets by classifier, supporting both inclusion (\"c.only[C]\") and exclusion (\"c.except[C]\"). The tree structure enables decidable disjointness reasoning: classifiers on separate branches are guaranteed to be disjoint regardless of unknown extensions elsewhere in the hierarchy. We formalize classifiers as an extension of System Capless, a core calculus for capture checking, introducing a classifier kind algebra based on intersection, union, and subtraction of classifier subtrees. We extend the operational semantics to model exception interception and establish type safety, effect safety, and handler coverage via a big-step proof, fully mechanized in Lean 4. Classifiers are implemented in the Scala 3 capture checker, and we demonstrate their use on standard library types and real-world effect exclusion patterns.",
      "title": "Classifying Capabilities (Extended Version)",
      "updated": "2026-07-27",
      "url": "https://arxiv.org/abs/2607.24504"
    },
    {
      "age_days": 4,
      "arxiv_id": "2607.23715",
      "authors": [
        "Shuqing Zhao"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23715",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-26",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "We report the design and end-to-end verification of first-class IEEE-754 binary32 (FP32) and bfloat16 (BF16) arithmetic for ARCH, a hardware description language intended to be generated by language models. Every operator - comparisons, conversions, add, sub, mul, and fused multiply-add (FMA) - is described once against a single bit-vector IR and rendered three ways from one source: synthesizable SystemVerilog, an SMT-LIB model, and a Lean 4 proof model. The three artifacts cannot drift apart structurally, and the residual per-node printer correspondence is machine-checked: a Yosys-to-SMT miter proves the emitted SystemVerilog equivalent to the SMT model for all 24 operators. Verification splits at the solver-tractability frontier: multiplier-free operators (comparisons, add/sub over all 2^64 inputs, conversions, and all binary BF16 arithmetic) are proved exhaustively equivalent to the SMT-LIB FloatingPoint theory; the SAT-hard multiplier-bearing operators (FP32 mul and FMA) are proved correctly rounded in Lean, sorry-free, against a value-level round-to-nearest-even specification over exact dyadic values. Physical characterization exposed the FMA as the timing outlier: its exact-wide 470-bit datapath does not pipeline in our flow. We reimplemented it as a bounded 98-bit guard/round/sticky datapath that pipelines to 268 MHz on Nangate45, and proved, in Lean and over all 2^96 inputs, that it is bit-identical to the exact-wide reference, so it inherits the reference's proven correct rounding. The equivalence is tractable precisely because the shared multiplier appears on both sides and cancels: neither a SAT solver nor the proof ever solves a multiplier equivalence. (The BF16 FMA is deliberately an FP32-accumulating fusion, characterized as exactly that.) All machine-checked claims are pinned to a tagged open-source release.",
      "title": "Formally Verified Synthesizable Floating-Point Data Types in ARCH HDL",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23715"
    },
    {
      "age_days": 4,
      "arxiv_id": "2607.23806",
      "authors": [
        "Sietse Schelpe"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23806",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-26",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Improving a language model today means retraining it: enormous compute, a new opaque model each cycle, non-deterministic output. We take the opposite path: the model stays frozen, and a persistent memory of verified solutions grows beside it. Once a problem family is solved and has passed an independent verification step that never consults the answer key, every new instance of that family is answered at zero generation tokens, bit-exact, deterministically. Across 180 fresh instances spanning nine problem families, four architectures from four vendors - dense and mixture-of-experts - each score 180/180 at zero generation tokens per answer: execution-bound capability decoupled from parameter scaling. A negative control attributes the capability fully to the memory: emptied, it solves nothing. The same verify-before-store contract holds for open-ended reasoning: 88/88 consistency-gated acceptances across all four models, machine-checked formal proof, and reasoning-method transfer at 77/80. Memory selection takes 1.4 microseconds; a full reuse completes in 6-23 ms at 36 mWh. Approximate similarity retrieval selects the wrong item 94.3% of the time on a 4,500-item verified store where exact addressing makes zero errors. The store also serves as working context at a scale no shipped engine matches: a 6,000,000-token movable window on a single 46 GB GPU at flat memory, where vLLM stops at 30,399 tokens and SGLang silently truncates past 32,000. On published benchmarks, frontier models remain far ahead of any 12B at raw from-scratch reasoning; on everything this system has solved and verified, the comparison inverts: a frontier API call pays a fresh generation pass on every query, forever, while verified reuse costs zero tokens and returns the identical bits every time. A public testbench with free, rate-limited access accompanies this report: https://corbenic-galahad-bench.hf.space",
      "title": "A Frozen 12B Beats Frontier Models on Verified Work: 100% Accuracy, 0 Tokens, Bit-Exact, Forever",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23806"
    },
    {
      "age_days": 5,
      "arxiv_id": "2607.22972",
      "authors": [
        "Evan Wang",
        "Simon Chess",
        "Sophie Szeto",
        "Theodore Meek"
      ],
      "content_date": "2026-07-25",
      "freshness": "fresh",
      "id": "arxiv:2607.22972",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-25",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Lean 4's grind tactic combines congruence closure, E-matching, and case-splitting into a single automated solver, and like any such solver, it relies on hand-tuned heuristics to decide what to instantiate and where to case-split. These heuristics are tempting targets for learning, but there is a catch: because grind's search is non-monotone, a learned heuristic that helps one proof can break another, and an always-on replacement usually nets out near zero. We avoid this by invoking a learned intervention only after stock grind has already failed: a failure-triggered cascade that, by construction, cannot lose a proof grind already had. We apply it to two of grind's internal decisions. A cost-aware E-matching filter solves slightly more problems and runs about 5% faster. A lookahead step proves five theorems it otherwise times out on. We also report the negative result that motivated the design: across four feature-based models, statically predicting the correct case split is no better than random, because whether a split explodes is a runtime property that the features do not capture. Our results suggest that learning within theorem-proving tactics is most effective as a mechanism for deciding when and how to spend bounded search, backed by a reliable symbolic fallback.",
      "title": "Learned Interventions in Lean 4 grind",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.22972"
    },
    {
      "age_days": 6,
      "arxiv_id": "2607.22524",
      "authors": [
        "Junye Ji"
      ],
      "content_date": "2026-07-24",
      "freshness": "fresh",
      "id": "arxiv:2607.22524",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-24",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "We formalize in Lean 4 the Kannan-Bachem Smith normal form algorithm for nonsingular square integer matrices. The program returns $S,U,U^{-1},V,V^{-1}$ and proves $UAV=S$, $U^{-1}SV^{-1}=A$, four inverse identities, the Smith divisibility conditions, and equality of $S$ with a canonical reference matrix. Stabilization terminates because each recursive pass strictly decreases the binary size of the active pivot; the outer algorithm recurses on the lower-right block. The computation also emits a flat trace of designated sign-magnitude arithmetic leaves. Branch conditions, quotients, Bezout data, and matrix entries are taken from the recorded primitive runs. Composite phases form their traces by concatenating the charge lists returned by the executed children. Verified self-delimiting codecs define the input and output sizes. Coefficient and work recurrences, closed by a kernel-checked polynomial-envelope calculus, give fixed polynomial bounds for both trace cost and the encoded length of the five output matrices. The theorem concerns these arithmetic primitives; structural operations and compiled Lean runtime are outside the model.",
      "title": "Machine-Checked Arithmetic Bit Complexity of the Kannan-Bachem Smith Normal Form in Lean 4",
      "updated": "2026-07-24",
      "url": "https://arxiv.org/abs/2607.22524"
    },
    {
      "age_days": 7,
      "arxiv_id": "2607.21187",
      "authors": [
        "Fred Mesnard",
        "Étienne Payet",
        "Wim Vanhoof"
      ],
      "content_date": "2026-07-23",
      "freshness": "fresh",
      "id": "arxiv:2607.21187",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-23",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "We present the interactions with an LLM (Large Language Model) aiming at proving that the square root of 2 is not a rational number in an LP (Logic Programming) context. We start from a few basic pure logic programming predicate definitions. We rely on the LPTP (Logic Program Theorem Prover) system for stating and proving properties about logic programs. As the proof language of LPTP is based on natural deduction, the proofs are human readable. In our case study, we sketch in LPTP the usual proof showing the irrationality of the square root of 2. Then we describe the interactions we had with the LLM. We end up with a complete formal proof, partially generated by an LLM and fully proof-checked by LPTP.",
      "title": "Case study: proving sqrt(2) irrational with LPTP and an LLM",
      "updated": "2026-07-23",
      "url": "https://arxiv.org/abs/2607.21187"
    },
    {
      "age_days": 8,
      "arxiv_id": "2607.20418",
      "authors": [
        "Jack Fitzsimons"
      ],
      "content_date": "2026-07-22",
      "freshness": "fresh",
      "id": "arxiv:2607.20418",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "lean_formal_proving_agents"
      ],
      "published": "2026-07-22",
      "score": 3.5,
      "source": "arxiv-ai4math-core",
      "summary": "Nikolov and Ullman asked whether k statistical queries on a universe of size T can be released under pure differential privacy with expected worst-coordinate error at the square-root rate suggested by known lower bounds. We prove their conjectured upper bound. For every database size n and privacy parameter $\\varepsilon>0$, there is an $\\varepsilon$-differentially private mechanism with expected error $O(\\min\\{1,\\sqrt{\\log(2T)\\log(2k)/(\\varepsilon n)}\\})$. This matches the lower-bound dependence in the standard high-dimensional regimes where those bounds apply; the shifted logarithms and outer minimum make the upper bound valid without additional parameter assumptions. The construction starts from a selection-only private multiplicative weights transcript, then replaces its probability mass function by a distance-penalized likelihood envelope. To prove that the modification preserves accuracy, a likelihood-level Maurey argument upper-bounds each Hamming-ball maximum by a small family of auxiliary PMW laws. Renyi moment bounds control nearby balls, a direct mixture bound controls distant balls, and grouping radii at the privacy scale prevents an additional $1/\\varepsilon$ factor in the error. The mechanism is information-theoretic. A companion Lean 4 development machine-checks the finite construction, pure privacy after deterministic decoding, and the displayed all-regimes upper bound.",
      "title": "Pure-DP Statistical Query Release at the Conjectured Square-Root Rate",
      "updated": "2026-07-22",
      "url": "https://arxiv.org/abs/2607.20418"
    },
    {
      "age_days": 3,
      "arxiv_id": "2607.25064",
      "authors": [
        "David Victor Feldman"
      ],
      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "arxiv:2607.25064",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "general_ai_math_reasoning"
      ],
      "published": "2026-07-27",
      "score": 1.9,
      "source": "arxiv-ai4math-core",
      "summary": "A compact convex body $K\\subseteq\\mathbb{R}^n$ is a first-order structure in two natural languages: $L_{\\mathrm{aff}}$, with a ternary betweenness relation and convex-combination operations, and the sparser $L_B$, with betweenness alone. Isomorphism means affine equivalence in the first and, by a theorem of Shiffman, projective equivalence in the second. We ask when elementary equivalence already determines the body. Main theorem: two compact convex bodies of any dimension, with no regularity hypotheses, are elementarily equivalent in $L_{\\mathrm{aff}}$ if and only if they are affinely equivalent. The proof rests on a definable compact family of gauges: simplices stationary for barycentric coordinates, with volume bounded below via the anticomplementary simplex. For $L_B$ we develop an interior von Staudt calculus, all quantifiers ranging over the body, making harmonic conjugacy, rational cross-ratio comparisons, and cross-ratio equality first-order; only the harmonic primitive is needed, with a single interior perspectivity effecting the change of scale. Consequences: the closed unit ball is separated from $\\{\\sum x_i^4\\le 1\\}$ in $L_B$ for every $n\\ge 2$; and in the plane, projective categoricity holds outright for convex polygons and for bodies with real-analytic, positively curved, non-conic boundary, the latter via a new finite projective invariant, the conic-cluster set: the points whose every boundary arc contains six co-conic extreme points. A general reduction isolates what remains of the projective conjecture: recovery of boundary coordinates in dimension at least three, and a definable compact gauge, obstructed exactly by non-compact projective symmetry, as on the quadric. We also classify the natural noncompact examples.",
      "title": "Elementary equivalence of convex bodies in affine and projective languages",
      "updated": "2026-07-27",
      "url": "https://arxiv.org/abs/2607.25064"
    },
    {
      "age_days": 4,
      "arxiv_id": "2607.23828",
      "authors": [
        "Thien Le",
        "Melanie Weber"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23828",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "general_ai_math_reasoning"
      ],
      "published": "2026-07-26",
      "score": 1.9,
      "source": "arxiv-ai4math-core",
      "summary": "We prove a conjecture of Monical, Tokcan, and Yong that every fixed positive power of the Vandermonde determinant is non-SNP in all sufficiently many variables, where a polynomial is non-SNP if there is a lattice point in its Newton polytope that does not appear with nonzero coefficient. This means our result proves that for every even power $k\\geq4$, there is always such a missing lattice monomial in large enough dimensions. The odd case follows from alternation, and the quadratic case was previously known. For every even power $k\\geq4$, we exhibit an explicit lattice point in the Newton polytope of $a_{δ_k}^k$ whose coefficient vanishes. The vanishing is obtained from a Dyson constant-term identity, proved using the finite-variable Jack scalar product and Macdonald's specialization formula. The key even-power construction and proof strategy arose from prompting with OpenAI Codex (GPT Sol 5.6 Extra High), a large language model; the complete transcript appears in the appendix. The authors subsequently checked and organized the argument. The accompanying Lean formalization is available at https://github.com/steven-le-thien/vandermonde-snp.",
      "title": "Powers of the Vandermonde determinant are eventually non-SNP",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23828"
    },
    {
      "age_days": 8,
      "arxiv_id": "2607.21653",
      "authors": [
        "Jian Hu",
        "Huiying Li",
        "Hao Zhang",
        "Binfeng Xu",
        "Yifan Zhang",
        "Shaokun Zhang",
        "Hemil Desai",
        "Michael Demoret",
        "Pavlo Molchanov",
        "Jan Kautz",
        "Yi Dong"
      ],
      "content_date": "2026-07-22",
      "freshness": "fresh",
      "id": "arxiv:2607.21653",
      "kind": "paper",
      "label": "adjacent",
      "matched_signals": [
        "reasoning_rl_distillation"
      ],
      "published": "2026-07-22",
      "score": 1.9,
      "source": "arxiv-ai4math-core",
      "summary": "Agentic reinforcement learning research is constant algorithm modification, new estimators, new pipeline stages, new rollout schemes, and in mainstream frameworks each change threads through layers of trainer, distributed backend, and rollout glue: the cost lands on the researcher at every iteration. Molt is a PyTorch-native training framework built to keep that cost small: a codebase compact and clean enough for a researcher to hold in their head, and for an AI coding assistant to read and reason about in its entirety, so the algorithm flow can be traced and changed end to end. The agent is an ordinary program, and one asynchronous loop trains multimodal and mixture-of-experts policies while never training on a token it did not generate, consistent in tokens, policy versions, and model semantics. Leanness does not cost performance: under a matched, fully asynchronous protocol, Molt is statistically comparable to a state-of-the-art Megatron-based stack. Molt is open source and provides recipes and containers at https://github.com/NVIDIA-NeMo/labs-molt.",
      "title": "Molt: A Scalable PyTorch-Native Training Framework for Agentic Reinforcement Learning",
      "updated": "2026-07-22",
      "url": "https://arxiv.org/abs/2607.21653"
    },
    {
      "age_days": 0,
      "authors": [
        "Sebastian Graf"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:2d0879c245c9",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: refactor: name the contract precondition clause `requires` (#14589)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/2d0879c245c9804aafe2b9b52dde1eca0712f889"
    },
    {
      "age_days": 0,
      "authors": [
        "Sebastian Graf"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:c7673e73d368",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: refactor: make the `Std.Internal.Do` loop invariant non-dependent (#14601)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/c7673e73d368472501289ee81acc8b68d3000621"
    },
    {
      "age_days": 0,
      "authors": [
        "Sofia Rodrigues"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:44d40a3d8ce0",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: fix: overwrite of known size after `Builder.stream` generator execution (#14541)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/44d40a3d8ce01ce5c15d1c0de5f896c9192ddc63"
    },
    {
      "age_days": 0,
      "authors": [
        "Sofia Rodrigues"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:55293f239b92",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: fix: deflake HTTP unknown size stream test (#14571)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/55293f239b92ff50aca6929d782026380ff15cac"
    },
    {
      "age_days": 0,
      "authors": [
        "Sebastian Graf"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:d19a5e526c29",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: footprint-based frame inference for vcgen via @[frameproc] (#14529)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/d19a5e526c29315b07683839666a8aa95d9f7e69"
    },
    {
      "age_days": 0,
      "authors": [
        "Sebastian Graf"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:0a67f6b94669",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: bind assertion arguments inline in `requires` and `invariant` (#14603)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/0a67f6b94669aa99183f0c7d5e15fc5c7f308a9f"
    },
    {
      "age_days": 0,
      "authors": [
        "Wojciech Różowski"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:103ef690cc30",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: add code quality metrics runner frontend (#14578)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/103ef690cc308618eb98476944213297cc5599a7"
    },
    {
      "age_days": 0,
      "authors": [
        "Sofia Rodrigues"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:f9541a74e218",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: add HTTP Server benchmark (#14268)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/f9541a74e218f128a0ef4cea78f20509833bcfe4"
    },
    {
      "age_days": 0,
      "authors": [
        "Julia Markus Himmel"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:5fe7f2454c32",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: simplify `bif` to `if` (#14588)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover/lean4/commit/5fe7f2454c32fe1605c6910f7140ce1f0a04f43b"
    },
    {
      "age_days": 0,
      "authors": [
        "Chris Henson"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:60af7185ecf3",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover-community/mathlib4",
      "score": 0.8,
      "source": "mathlib4-github",
      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: fix: increase priority of show elaboration (#42264)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover-community/mathlib4/commit/60af7185ecf32ed5cab100f9960f1d588b9a6cab"
    },
    {
      "age_days": 0,
      "authors": [
        "Yi.Yuan"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:2b4f1fd3b3fd",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-30",
      "repo": "leanprover-community/mathlib4",
      "score": 0.8,
      "source": "mathlib4-github",
      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: feat: generalize `range_lt_top_of_det_eq_zero` (#42055)",
      "updated": "2026-07-30",
      "url": "https://github.com/leanprover-community/mathlib4/commit/2b4f1fd3b3fd72117ba25a906f40d5021e3b44a6"
    },
    {
      "age_days": 0,
      "authors": [
        "Xavier Roblot"
      ],
      "content_date": "2026-07-30",
      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:481daa6fe9ab",
      "kind": "github_update",
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        "Leonardo de Moura"
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      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: fix: missing check at kernel inductive declaration (#14577)",
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      "url": "https://github.com/leanprover/lean4/commit/a39eab69e1eee9ad38f4efe507907b1026a77808"
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        "Mac Malone"
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      "repo": "leanprover/lean4",
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      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: fix: ci: early exit rebootstrap on no change to stage0 (#14575)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover/lean4/commit/aa89d2777fb4342ff3084502e8f2d5a01aa17222"
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    {
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        "Wojciech Różowski"
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      "repo": "leanprover/lean4",
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      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: handle stacked dependent projections in `cbv` that compose into dependent projection (#14567)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover/lean4/commit/b1722adad3d00ad4443a08709b1efb93a78b477c"
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    {
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        "Wojciech Różowski"
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      "id": "github:leanprover/lean4:7f7ecd721147",
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      "repo": "leanprover/lean4",
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      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: do not show deprecated syntax warnings inside of deprecated definitions (#14533)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover/lean4/commit/7f7ecd721147fb5f61f46825a859b8721aa8fa7b"
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    {
      "age_days": 2,
      "authors": [
        "Wojciech Różowski"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:dfeeb611bc5a",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-28",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: add basic code quality infrastructure data types (#14568)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover/lean4/commit/dfeeb611bc5a352e49670c888b26a9ce12fe8ecc"
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    {
      "age_days": 2,
      "authors": [
        "Chris Henson"
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      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:5eec30bc56ed",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
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      "repo": "leanprover-community/mathlib4",
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      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: perf(LinearAlgebra/Pi): golf `Matrix.ker_diagonal_toLin'` (#42199)",
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      "url": "https://github.com/leanprover-community/mathlib4/commit/5eec30bc56ed5a23be2e27c544a949ba0bceddeb"
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    {
      "age_days": 2,
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        "Anatole Dedecker"
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      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:c7202db459de",
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      "matched_signals": [],
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      "repo": "leanprover-community/mathlib4",
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      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: feat: define Fredholm operators between TVSs (#41189)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover-community/mathlib4/commit/c7202db459de001e1f4dcbe9cc244a2be198010f"
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    {
      "age_days": 2,
      "authors": [
        "Yaël Dillies"
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      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:0ce898f5af28",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-28",
      "repo": "leanprover-community/mathlib4",
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      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: feat(RingTheory): bialgebra homs `R[G] → R[H]` are in bijection with group homs `G → H` (#41995)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover-community/mathlib4/commit/0ce898f5af28a9a72054a68b7f6331224e7dc767"
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    {
      "age_days": 2,
      "authors": [
        "Michael Rothgang"
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      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "github:leanprover-community/mathlib4:01cceef4309e",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-28",
      "repo": "leanprover-community/mathlib4",
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      "source": "mathlib4-github",
      "summary": "Recent commit on leanprover-community/mathlib4.",
      "title": "leanprover-community/mathlib4: chore: make `ModelProd` and `ModelPi` implicit_reducible (#42038)",
      "updated": "2026-07-28",
      "url": "https://github.com/leanprover-community/mathlib4/commit/01cceef4309e0b28bde5dd951c72960f5282a1ac"
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    {
      "age_days": 3,
      "authors": [
        "Wojciech Różowski"
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      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:8db181099406",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: refactor: split `registerParametricAttribute` into extension creation and registration (#14344)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/8db18109940653665e63a5bed7da56e4ee182f86"
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    {
      "age_days": 3,
      "authors": [
        "Sebastian Graf"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:9ec149eff60a",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: perf: compare `forbiddenTks` by pointer first in parser cache keys (#14562)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/9ec149eff60a8012285622ea52c8adb6dfc00c2f"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:76e5ec53d211",
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      "matched_signals": [],
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      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: support code actions in linters (#14402)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/76e5ec53d211e9c6d8aec071bb427bc482515d3d"
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    {
      "age_days": 3,
      "authors": [
        "Sebastian Graf"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:6832de296014",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: intrinsic verification syntax for do-notation loops and def contracts (#14389)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/6832de296014011d0f126e3fa32a8366b5299b35"
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    {
      "age_days": 3,
      "authors": [
        "Wojciech Różowski"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:7739f09a3e40",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: feat: display deprecated module warnings in the right positions (#14564)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/7739f09a3e400fceb684a8fd64c8080d9355ab87"
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    {
      "age_days": 3,
      "authors": [
        "Lean stage0 autoupdater"
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      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:bb3c22f9fb28",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: update stage0",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/bb3c22f9fb288c803f7d5ba96fec210976db65c5"
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    {
      "age_days": 3,
      "authors": [
        "Julia Markus Himmel"
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      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:889846da643a",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: reenable check for `since` field in deprecations (#14572)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/889846da643a12e3ee2cbb0edf7f36ddd62b9e10"
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    {
      "age_days": 3,
      "authors": [
        "Julia Markus Himmel"
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      "freshness": "fresh",
      "id": "github:leanprover/lean4:779f04e73e55",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: disable check for `since` field in deprecations (#14566)",
      "updated": "2026-07-27",
      "url": "https://github.com/leanprover/lean4/commit/779f04e73e553583f9496558a991ab90911acce5"
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    {
      "age_days": 3,
      "authors": [
        "Julia Markus Himmel"
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      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "github:leanprover/lean4:e422bfd29662",
      "kind": "github_update",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-27",
      "repo": "leanprover/lean4",
      "score": 0.8,
      "source": "lean4-github",
      "summary": "Recent commit on leanprover/lean4.",
      "title": "leanprover/lean4: chore: `typeChanced` syntax for deprecated attribute (#14570)",
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      "url": "https://github.com/leanprover/lean4/commit/e422bfd29662fe7ef890f90558725f8b8b708cee"
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    {
      "age_days": 2,
      "arxiv_id": "2607.25262",
      "authors": [
        "Wenhua Li",
        "Darius Foo",
        "Quang Trung Ta",
        "Wei-Ngan Chin"
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      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.25262",
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      "published": "2026-07-28",
      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "Type safety has traditionally rested on carefully crafted type systems, under the motto \"well-typed programs cannot go wrong\". Modern demands push type systems past this basic guarantee: toward memory safety (e.g., Rust), stronger data-structure invariants (e.g., GADTs), and broader typability (e.g., MLstruct). The motto absorbs each such property by enlarging the set of states deemed \"wrong\", but collapses them into one binary verdict: heap ownership, flow-sensitive changes to a variable's type, and the gap between a recoverable and a fatal error are relational, stateful facts about intermediate states that one verdict cannot tell apart. Worse, each demand typically brings its own extension, making it hard to say what each guarantees or how they combine. Floyd-Hoare logic supplies a unified foundation. We present a framework for type-safety verification built from four ingredients: (i) case specifications for path-sensitive typing; (ii) separation types, inspired by separation logic, for flow-sensitive type mutation and must-aliasing; (iii) a disciplined distinction between Err (runtime error values our types track) and Abrt (compile-time errors), yielding the refined motto well-typed programs must never abort; and (iv) type predicates for data-structure invariants. Since all four are ordinary types in one Boolean algebra rather than separate extensions, the framework subsumes both GADTs and liquid types within one type logic, spanning weak specifications that tolerate Err to strong ones that eliminate it. Subtyping reduces to one decidable emptiness test, so a single lightweight procedure serves the whole framework with no SMT oracle in its trusted base. We formalise the Hoare rules and prove soundness in a machine-checked Lean mechanisation; by proof reflection it yields a self-certifying type-checker, evaluated on a benchmark suite.",
      "title": "Type Safety via Hoare Logic with Separation and Pure Types",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.25262"
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      "authors": [
        "Adam Y Shavit"
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      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.26297",
      "kind": "paper",
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      "published": "2026-07-28",
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      "source": "arxiv-ai4math-core",
      "summary": "Patient-reported pain location is diagnostically decisive for some presentations and nearly uninformative for others. The prevailing account treats this as a single gradient of diagnostic utility governed by anatomical complexity. That explanation conflates three epistemically distinct failures of localization, each with its own mathematical structure, optimal instrument, and public-health consequence. In anatomical multiplexing (a), many structures share one location: a non-identifiable inverse problem. In delocalized amplification (b) --- clinically, central sensitization or nociplastic pain --- a centrally driven pain-behaviour pattern replaces the peripheral generator: a change of generative model. In referred/atypical displacement (c), location shifts in a systematic, person-dependent way, as in referred pain and in atypical presentations: a covariate-dependent bias. The three behave differently under inverse-problem, information-theoretic, decision-theoretic, and neural-field models, and demand different remedies. A single principle unifies them: they are one Bayesian inference problem failing at different nodes, meaning different points in one generative model: the likelihood, the model class, and the prior and loss. Observation over time increases the recoverable information. A fourth node, the report itself, carries this paper's main formal contribution --- a spatial Bayesian model of where pain is said to be, distinct from where it is felt. Re-examination further finds that the published \"high-utility\" accuracy band leans on overstated specificity, so the gradient is real but flatter than drawn. The account sits on a \"why-location-fails\" axis, a synthesis distinct from the nociceptive/neuropathic/nociplastic taxonomy (Kosek et al., 2016).",
      "title": "Three Failures of Pain Location: Why the Diagnostic Utility of Symptom Localization Is Not One Thing",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.26297"
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      "authors": [
        "Kazuki Ikeda"
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      "content_date": "2026-07-28",
      "freshness": "fresh",
      "id": "arxiv:2607.26331",
      "kind": "paper",
      "label": "negative",
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      "published": "2026-07-28",
      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "We construct quantum Turing patterns in Lindblad lattice dynamics and establish a rigorous theory of their nonlinear order and quantum fluctuations. For an explicit completely positive family with finite-range couplings, the first-moment equations undergo a supercritical instability at a nonzero wave number and admit analytic site- and bond-centered commensurate stripe branches. These branches are locally asymptotically stable in their reflection-fixed period-cell spaces, and projected coherent states exhibit extensive Bragg order on every bounded time interval in the semiclassical limit. We prove $O(N^{-1/2})$ convergence of microscopic covariances to a nonautonomous Gaussian Lyapunov flow, transferring strict partial-transpose uncertainty violations to sufficiently large $N$. In the homogeneous Gaussian sector, a single dimensionless ratio controls both the Turing stability determinant and the logarithmic negativity of opposite momenta, relating wavelength selection directly to quantum entanglement. Differential transport shifts the strongest opposite-momentum correlations from the infrared to the selected Turing scale. Numerical continuation and two-dimensional simulations display stripe, spot, and labyrinth morphologies whose Fourier modes and fluctuation spectra concentrate at the same selected wave numbers.",
      "title": "Quantum Turing Patterns",
      "updated": "2026-07-28",
      "url": "https://arxiv.org/abs/2607.26331"
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    {
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      "authors": [
        "Diego Fernandez Arias",
        "Dev Prashant Mistry",
        "Ren Wang",
        "Yibo Hu"
      ],
      "content_date": "2026-07-27",
      "freshness": "fresh",
      "id": "arxiv:2607.24893",
      "kind": "paper",
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      "published": "2026-07-27",
      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: a poisoned tool hides encrypted fragments in its observations, spreads them across several agents, and an external step reassembles and executes them after the run. Per-step safety checks that judge each action in isolation may fail to recognize the complete distributed payload. We investigate how early such an attack can be detected while the run is still unfolding, and how robustly it can be caught once its most obvious cues are stripped away. We build a working instance on a hierarchical multi-agent system, run it under benign and attacked conditions across five language models and two task domains, and record when each fragment is injected and when the payload is assembled and executed. Detection is a race against assembly. Before the first fragment is injected, attacked and benign runs are indistinguishable; once injection begins, a prefix detector flags $99.3\\%$ of successful attacks with a median of five steps remaining and a $10.3\\%$ safe-run false-positive rate. Because assembly occurs only after the run, these alarms arrive in time to abort nearly every successful attack. We then measure how much of that warning rests on removable surface cues of the attack rather than on its distributed structure. Generic zero-shot and behavior-trained detectors provide almost no warning at all; the detectors that do work lean in part on removable surface cues, chiefly the ciphertext's length and entropy, and once the entropy cue is removed from the payload and the length features from the detector, detection arrives later and transfers poorly across domains, though a fine-tuned model recovers some of the loss.",
      "title": "Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study",
      "updated": "2026-07-27",
      "url": "https://arxiv.org/abs/2607.24893"
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      "authors": [
        "Gyeongwon Jeong",
        "Seonghun Park",
        "Jihoon Hyun",
        "Sang-il Oum",
        "Hongseok Yang"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23500",
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      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "Razborov's flag algebra method is a powerful tool for proving asymptotic inequalities in extremal graph theory, often reducing the task to finding a finite certificate by semidefinite programming. We present a machine-checked formalization of the method for finite simple graphs, together with a certificate-to-proof compiler that turns externally generated certificate data into algebraic proofs checked by Lean. The formalization covers the foundations of the method: partially labeled graphs, their densities in large graphs, the quotient algebra of density expressions, graph-limit semantics through positive homomorphisms, and the downward operators used to average out labels. The compiler treats the external semidefinite programming output as candidate data rather than trusted input: Lean independently computes the required density and multiplication facts, verifies positive semidefiniteness exactly over $\\mathbb{Q}$, and carries out the algebraic normalization steps of flag-algebra proofs. Our case studies yield formal proofs of seven Turán-type upper bounds, including Mantel's theorem and the Erdős pentagon theorem, a $C_4$-density bound for triangle-free graphs, and edge-density bounds for $K_4$-free, $K_5$-free, and $C_5$-free graphs. Independently of the compiler, we formalize the matching constructions that complete the exact Turán densities of Mantel's theorem and the Erdős pentagon theorem, and prove two inequalities of Goodman. Our constrained semantics also prompted a meta-theoretic comparison of two ways of imposing graph constraints: building a hereditary constraint into the flag algebra from the start, or testing inequalities afterward on constrained graph limits with labels chosen at random. We state the resulting root-plantability criterion characterizing when the two approaches agree; a forthcoming paper will present the complete account.",
      "title": "Formalizing Flag Algebras in Lean",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23500"
    },
    {
      "age_days": 4,
      "arxiv_id": "2607.23427",
      "authors": [
        "Seiichiro Kusuoka",
        "Shuta Nakajima"
      ],
      "content_date": "2026-07-26",
      "freshness": "fresh",
      "id": "arxiv:2607.23427",
      "kind": "paper",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-26",
      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "In this note, we consider the Sherrington--Kirkpatrick model with deterministic external field. Let $q=q(β,h)$ denote the solution of the replica-symmetric self-consistency equation \\[ q=\\mathbb E\\tanh^2\\!\\left(h+β\\sqrt q\\,Z\\right), \\qquad Z\\sim N(0,1), \\] where $β$ and $h$ are inverse temperature and external field, respectively. By refining Lata\\la' s argument, previously limited to \\(β< \\frac{1}{2}\\), and using the Kearns--Saul inequality, we prove overlap concentration and convergence of the free energy to the replica symmetric formula with error \\(O(N^{-1})\\) whenever \\[ β^2\\frac{q}{{\\rm arctanh}q}<1. \\] Note that for any $β<1$ and $h\\in \\mathbb R$, the condition above is satisfied. Moreover, for every nonzero $h$, this region contains a nonempty interval with $β>1$.",
      "title": "A note on Lata\\la's argument in SK model",
      "updated": "2026-07-26",
      "url": "https://arxiv.org/abs/2607.23427"
    },
    {
      "age_days": 8,
      "arxiv_id": "2607.20382",
      "authors": [
        "Eva McCord",
        "Ernest Pedapati",
        "Zag ElSayed"
      ],
      "content_date": "2026-07-22",
      "freshness": "fresh",
      "id": "arxiv:2607.20382",
      "kind": "paper",
      "label": "negative",
      "matched_signals": [],
      "published": "2026-07-22",
      "score": 0.5,
      "source": "arxiv-ai4math-core",
      "summary": "Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reconciliation, and loosely integrated systems, resulting in limited state visibility, delayed reporting, and increased operational risk. These challenges are particularly pronounced in multi-day assays such as Luminex-based quantification of Fragile X Messenger Ribonucleoprotein (FMRP), where HIPAA-compliant data governance, deterministic workflow progression, and coordinated communication across laboratory and clinical teams are required. This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes biospecimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability. The system integrates a self-hosted Supabase/PostgreSQL stack deployed within hospital-controlled infrastructure, hybrid edge-internal isolation with encrypted tunneling and loopback-only services, and bi-directional REDCap synchronization. A unified MRN-UUIDv7 identifier framework with QR-based tracking ensures traceable clinical-research linkage under PHI residency constraints. FMRP-LEAN incorporates automated statistical QC pre-screening and a governance-constrained AI operations module that operates exclusively on aggregate projections, with deterministic fallback guarantees. Deployment demonstrates improved workflow observability, reduced QC latency, and enhanced cross-role transparency between laboratory technicians, research coordinators, and patient-facing teams. The architecture provides a reproducible model for secure, state-explicit, and AI-augmented clinical research workflows in regulated healthcare environments.",
      "title": "FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization",
      "updated": "2026-07-22",
      "url": "https://arxiv.org/abs/2607.20382"
    },
    {
      "age_days": 6,
      "arxiv_id": "2607.22100",
      "authors": [
        "Lorenzo Concina",
        "Seraphina Fong",
        "Marco Matassoni",
        "Alessio Brutti"
      ],
      "content_date": "2026-07-24",
      "freshness": "fresh",
      "id": "arxiv:2607.22100",
      "kind": "paper",
      "label": "negative",
      "matched_signals": [
        "negative:generic_llm_rag"
      ],
      "published": "2026-07-24",
      "score": -2.5,
      "source": "arxiv-ai4math-core",
      "summary": "Lightweight projectors are an established way to connect pre-trained speech encoders with large language models (LLMs), mapping acoustic features into token-level embeddings for tasks like ASR and spoken question answering. Existing systems, however, typically only support a few languages and are often limited to English. We introduce MEUSLI, the first open-science multilingual projector family that links a Whisper encoder with open-source multilingual LLMs, enabling fully open-source end-to-end ASR in 28 European languages. MEUSLI extends prior monolingual pipelines, delivering strong results across high- and low-resource languages. Using proper continual leaning techniques, MEUSLI can be easily extended to other languages not seen in training. We further demonstrate that the MEUSLI projector can be leveraged beyond ASR, enabling multilingual speech translation and topic identification with only a few hours of task specific supervision per language. Overall, MEUSLI provides a solid foundation for multilingual speech understanding tasks, supporting scalable and inclu- sive open-source SpeechLLM",
      "title": "MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond",
      "updated": "2026-07-24",
      "url": "https://arxiv.org/abs/2607.22100"
    }
  ],
  "lookback_days": 21,
  "schema": "ai4math-radar-run-v1",
  "timezone": "America/Los_Angeles"
}